Python Pandas 使用来自同一 DF 的其他列的值创建新列

Question

寻找解决方案是一个漫长的夜晚，感谢您的帮助。

具有以下 df

提案1_金额	提案2_金额	提案3_金额	接受的建议
1000	2000	3000	3
5000	5200	4000	2
3000	2400	1120	1

我需要使用来自接受的相应列的金额构建一个新列，它会是这样的：

提案1_金额	提案2_金额	提案3_金额	接受的建议	接受金额
1000	2000	3000	3	3000
5000	5200	4000	2	5200
1450	2400	1120	1	1450

我发现一些示例在新列具有固定值时可以正常工作，但在这种情况下，该值来自同一 df 上的另一列。

谢谢，vv

Answer 1

我能想到的最快的解决方案：

df['accepted_amount'] = df.apply(lambda row: row.iloc[row['accepted_proposal']-1],axis=1)

编辑：因为我对取决于列顺序的解决方案感到不安，所以这里有一个稍微冗长但更动态的解决方案：

df['accepted_amount']=df.apply(lambda row: row[['proposal1_amount','proposal2_amount','proposal3_amount']].iloc[row['accepted_proposal']-1],axis=1)

Answer 2

您可以使用numpy.choose轻松完成此操作。

print(df)
   proposal1_amount  proposal2_amount  proposal3_amount  accepted_proposal
0              1000              2000              3000                  3
1              5000              5200              4000                  2
2              3000              2400              1120                  1

# create 2d array of our choices (which corresponds to our amounts)
choices = df.filter(regex="proposal\d_amount").to_numpy()

# subtract 1 from "accepted_proposal" so they line up with indices in choices array
#  (we want these 0-indexed, not 1-indexed)
a = df["accepted_proposal"] - 1

# np.choose does all the heavy lifting, assign output to new column
df["accepted_amount"] = np.choose(a, choices)

print(df)
   proposal1_amount  proposal2_amount  proposal3_amount  accepted_proposal  accepted_amount
0              1000              2000              3000                  3             3000
1              5000              5200              4000                  2             5200
2              3000              2400              1120                  1             3000

np.choose将在功能上迭代每行选择（例如迭代每个“proposalN_amount”），然后从accepted_proposal - 1 。 请参阅np.choose的文档

Answer 3

proposal1_amount=[1000,5000,1450]
proposal2_amount=[2000,5200,2400]
proposal3_amount=[3000,4000,1120]
accepted_proposal=[3,2,1]
df=pd.DataFrame({'proposal1_amount': proposal1_amount,'proposal2_amount': proposal2_amount,'proposal3_amount':proposal3_amount,'accepted_proposal':accepted_proposal})
df['accepted_proposal']=df['accepted_proposal'].astype(int)
df=df.assign(accepted_amount=df.apply(lambda row:     row.iloc[row['accepted_proposal']-1], axis=1))    
print(df)

 output:

    proposal1_amount  proposal2_amount  proposal3_amount  accepted_proposal  
 0              1000              2000              3000                  3   
 1              5000              5200              4000                  2   
 2              1450              2400              1120                  1   

     accepted_amount  
 0             3000  
 1             5200  
 2             1450